G2 Comparability.
We argue that model designers can make a decision on whether a specific dataset, model architecture, or training procedure outperforms another only with the help of visual comparison.
Thus, making these configurations comparable in the visualization is another important design guideline we propose for bias identification in large label spaces.
This includes comparing bias indicators between models or datasets for individual labels, as well as looking at the bias-distribution across different models or datasets.
Here, we can draw inspiration from Tamara Munzner, who states: the capability of inspecting a single target in detail is often necessary, but not sufficient, for comparison [1]}.
We, thus, propose to visualize both the distribution of correlation values and values for individual labels to enable a visual comparison of different configurations.
